Can microvascular invasion in hepatocellular carcinoma be predicted by diagnostic imaging? A critical review

Imaging prognostic biomarkers in hepatocellular carcinoma: a comprehensive review

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide with its incidence on the rise globally. This paper provides a comprehensive review of prognostic imaging markers in HCC, emphasizing their role in risk stratification and clinical decision-making. We explore quantitative and qualitative criteria derived from imaging studies, such as computed tomography and magnetic resonance imaging, which can offer valuable insights into the biological behavior of the tumor. While many of these markers are not yet widely integrated into current clinical guidelines, they represent a promising future direction for approaching this highly heterogeneous cancer. However, standardization and validation of these markers remain important challenges. We conclude by emphasizing the importance of ongoing research to enhance clinical practices and improve outcomes for patients with HCC.

The potential of 18F-FDG PET/CT metabolic parameter-based nomogram in predicting the microvascular invasion of hepatocellular carcinoma before liver transplantation

PURPOSE

Microvascular invasion (MVI) is a critical factor in predicting the recurrence and prognosis of hepatocellular carcinoma (HCC) after liver transplantation (LT). However, there is a lack of reliable preoperative predictors for MVI. The purpose of this study is to evaluate the potential of an 18F-FDG PET/CT-based nomogram in predicting MVI before LT for HCC.

METHODS

83 HCC patients who obtained 18F-FDG PET/CT before LT were included in this retrospective research. To determine the parameters connected to MVI and to create a nomogram for MVI prediction, respectively, Logistic and Cox regression models were applied. Analyses of the calibration curve and receiver operating characteristic (ROC) curves were used to assess the model's capability to differentiate between clinical factors and metabolic data from PET/CT images.

RESULTS

Among the 83 patients analyzed, 41% were diagnosed with histologic MVI. Multivariate logistic regression analysis revealed that Child-Pugh stage, alpha-fetoprotein, number of tumors, CT Dmax, and Tumor-to-normal liver uptake ratio (TLR) were significant predictors of MVI. A nomogram was constructed using these predictors, which demonstrated strong calibration with a close agreement between predicted and actual MVI probabilities. The nomogram also showed excellent differentiation with an AUC of 0.965 (95% CI 0.925-1.000).

CONCLUSION

The nomogram based on 18F-FDG PET/CT metabolic characteristics is a reliable preoperative imaging biomarker for predicting MVI in HCC patients before undergoing LT. It has demonstrated excellent efficacy and high clinical applicability.

Dynamic contrast-enhanced ultrasonography with sonazoid predicts microvascular invasion in early-stage hepatocellular carcinoma

OBJECTIVE

Microvascular invasion (MVI) is an independent risk factor for the early recurrence and poor survival of hepatocellular carcinoma (HCC). This study aims to investigate the potential clinical value of dynamic contrast-enhanced ultrasound (DCE-ultrasound)-Sonazoid in pre-operatively assessing MVI in HCC.

METHODS AND MATERIALS

This single centre prospective study included 140 patients with histopathologically confirmed single HCC lesions. Patients were classified according to the post-operative pathological information presence of MVI: MVI+ group (n = 32) and MVI- group (n = 108). All patients underwent DCE-ultrasound within 1 week before surgery. The quantitative perfusion parameters of HCC lesions, margins of HCC lesions, and distal liver parenchyma were obtained and analyzed.

RESULTS

Clinicopathological (serum alpha-fetoprotein, Des-gamma-carboxyprothrombin, and pathological grade) and grayscale imaging features (tumor size) were significantly different between the MVI+ and MVI- groups (p < 0.05). Further quantitative analysis showed that when comparing the MVI+ and MVI- groups, half-decrease time and wash-out rate of HCC lesions and peak enhancement in the arterial phase of difference between the margin area of HCC and distal liver parenchyma were significantly different (p = 0.045, p = 0.035, and p = 0.023, respectively). Combining the above three quantitative parameters, the accuracy, sensitivity, specificity, positive-predictive value, and negative-predictive value were 69.3% (97/140), 37.8% (17/45), 84.3% (80/95), 53.1% (17/32), 74.1% (80/108), respectively.

CONCLUSION

DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions.

ADVANCES IN KNOWLEDGE

DCE-ultrasound with quantitative perfusion analysis has the potential to predict MVI in HCC lesions.

CT radiomics for prediction of microvascular invasion in hepatocellular carcinoma: A systematic review and meta-analysis

The power of computed tomography (CT) radiomics for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) demonstrated in current research is variable. This systematic review and meta-analysis aim to evaluate the value of CT radiomics for MVI prediction in HCC, and to investigate the methodologic quality in the workflow of radiomics research. Databases of PubMed, Embase, Web of Science, and Cochrane Library were systematically searched. The methodologic quality of included studies was assessed. Validation data from studies with Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) statement type 2a or above were extracted for meta-analysis. Eleven studies were included, among which nine were eligible for meta-analysis. Radiomics quality scores of the enrolled eleven studies varied from 6 to 17, accounting for 16.7%-47.2% of the total points, with an average score of 14. Pooled sensitivity, specificity, and Area Under the summary receiver operator Characteristic Curve (AUC) were 0.82 (95% CI 0.77-0.86), 0.79 (95% CI 0.75-0.83), and 0.87 (95% CI 0.84-0.91) for the predictive performance of CT radiomics, respectively. Meta-regression and subgroup analyses showed radiomics model based on 3D tumor segmentation, and deep learning model achieved superior performances compared to 2D segmentation and non-deep learning model, respectively (AUC: 0.93 vs. 0.83, and 0.97 vs. 0.83, respectively). This study proves that CT radiomics could predict MVI in HCC. The heterogeneity of the included studies precludes a definition of the role of CT radiomics in predicting MVI, but methodology warrants uniformization in the radiology community regarding radiomics in HCC.

A nomogram model integrating LI-RADS features and radiomics based on contrast-enhanced magnetic resonance imaging for predicting microvascular invasion in hepatocellular carcinoma falling the Milan criteria

PURPOSE

To establish and validate a nomogram model incorporating both liver imaging reporting and data system (LI-RADS) features and contrast enhanced magnetic resonance imaging (CEMRI)-based radiomics for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) falling the Milan criteria.

METHODS

In total, 161 patients with 165 HCCs diagnosed with MVI (n = 99) or without MVI (n = 66) were assigned to a training and a test group. MRI LI-RADS characteristics and radiomics features selected by the LASSO algorithm were used to establish the MRI and Rad-score models, respectively, and the independent features were integrated to develop the nomogram model. The predictive ability of the nomogram was evaluated with receiver operating characteristic (ROC) curves.

RESULTS

The risk factors associated with MVI (P<0.05) were related to larger tumor size, nonsmooth margin, mosaic architecture, corona enhancement and higher Rad-score. The areas under the ROC curve (AUCs) of the MRI feature model for predicting MVI were 0.85 (95% CI: 0.78-0.92) and 0.85 (95% CI: 0.74-0.95), and those for the Rad-score were 0.82 (95% CI: 0.73-0.90) and 0.80 (95% CI: 0.67-0.93) in the training and test groups, respectively. The nomogram presented improved AUC values of 0.87 (95% CI: 0.81-0.94) in the training group and 0.89 (95% CI: 0.81-0.98) in the test group (P<0.05) for predicting MVI. The calibration curve and decision curve analysis demonstrated that the nomogram model had high goodness-of-fit and clinical benefits.

CONCLUSIONS

The nomogram model can effectively predict MVI in patients with HCC falling within the Milan criteria and serves as a valuable imaging biomarker for facilitating individualized decision-making.

Using pre-operative radiomics to predict microvascular invasion of hepatocellular carcinoma based on Gd-EOB-DTPA enhanced MRI

Objectives

We aimed to investigate the value of performing gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid (Gd-EOB-DTPA) enhanced magnetic resonance imaging (MRI) radiomics for preoperative prediction of microvascular invasion (MVI) of hepatocellular carcinoma (HCC) based on multiple sequences.

Methods

We randomly allocated 165 patients with HCC who underwent partial hepatectomy to training and validation sets. Stepwise regression and the least absolute shrinkage and selection operator algorithm were used to select significant variables. A clinicoradiological model, radiomics model, and combined model were constructed using multivariate logistic regression. The performance of the models was evaluated, and a nomogram risk-prediction model was built based on the combined model. A concordance index and calibration curve were used to evaluate the discrimination and calibration of the nomogram model.

Results

The tumour margin, peritumoural hypointensity, and seven radiomics features were selected to build the combined model. The combined model outperformed the radiomics model and the clinicoradiological model and had the highest sensitivity (90.89%) in the validation set. The areas under the receiver operating characteristic curve were 0.826, 0.755, and 0.708 for the combined, radiomics, and clinicoradiological models, respectively. The nomogram model based on the combined model exhibited good discrimination (concordance index = 0.79) and calibration.

Conclusions

The combined model based on radiomics features of Gd-EOB-DTPA enhanced MRI, tumour margin, and peritumoural hypointensity was valuable for predicting HCC microvascular invasion. The nomogram based on the combined model can intuitively show the probabilities of MVI.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-022-00855-w.

Evaluation of perfusion CT and dual-energy CT for predicting microvascular invasion of hepatocellular carcinoma

PURPOSE

Evaluation of perfusion CT and dual-energy CT (DECT) quantitative parameters for predicting microvascular invasion (MVI) of hepatocellular carcinoma (HCC) prior to surgery.

METHODS

This prospective single-center study included fifty-six patients (44 men; median age 67; range 31-84) who provided written informed consent. Inclusion criteria were (1) treatment-naïve patients with a diagnosis of HCC, (2) an indication for hepatic resection, and (3) available arterial DECT phase and perfusion CT (GE revolution HD-GSI). Iodine concentrations (IC), arterial density (AD), and 9 quantitative perfusion parameters for HCC were correlated to pathological results. Radiological parameters based principal component analysis (PCA), corroborated by unsupervised heatmap classification, was meant to deliver a model for predicting MVI in HCC. Survival analysis was performed using univariable log-rank test and multivariable Cox model, both censored at time of relapse.

RESULTS

58 HCC lesions were analyzed (median size 42.3 mm; range of 20-140). PCA showed that the radiological model was predictive of tumor grade (p = 0.01), intratumoral MVI (p = 0.004), peritumoral MVI (p = 0.04), MTM (macrotrabecular-massive) subtype (p = 0.02), and capsular invasion (p = 0.02) in HCC. Heatmap classification of HCC showed tumor heterogeneity, stratified into three main clusters according to the risk of relapse. Survival analysis confirmed that permeability surface-area product (PS) was the only significant independent parameter, among all quantitative tumoral CT parameters, for predicting a risk of relapse (Cox p value = 0.004).

CONCLUSION

A perfusion CT and DECT-based quantitative imaging profile can provide a diagnosis of histological MVI in HCC. PS is an independent parameter for relapse.

CLINICAL TRIALS

ClinicalTrials.gov: NCT03754192.

Preoperative Assessment of Abdominal Adipose Tissue to Predict Microvascular Invasion in Small Hepatocellular Carcinoma

BACKGROUND AND AIMS

Microvascular invasion (MVI) affects recurrence after treatment of small hepatocellular carcinoma (sHCC) of ≤3 cm in size. The present study aimed to investigate whether abdominal subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT) are associated with MVI in patients with sHCC.

METHODS

A total of 124 patients with pathologically-confirmed sHCC diagnosed on surgical resection at the First Hospital Affiliated to Army Military University were recruited and divided into two groups according to MVI classification criteria (i.e., MVI-positive or MVI-negative). The SAT, VAT, and IMAT areas at the lumbar 3 vertebral level were imaged with abdominal computed tomography and measured using ImageJ software. Their association with MVI in sHCC was analyzed.

RESULTS

Of the 124 patients with sHCC, 67 were MVI-positive and 57 were MVI-negative. Univariate analysis revealed a significant difference in the abdominal VAT and SAT between the MVI-positive and MVI-negative groups (p<0.05), with an area under the receiver operating characteristic curve of 0.76 and 0.65, respectively.

CONCLUSIONS

The results of this study suggest that the areas of abdominal SAT and VAT are of significant clinical value because they can effectively predict the MVI status in patients with sHCC.

New Insights Into a Classification-Based Microvascular Invasion Prediction Model in Hepatocellular Carcinoma: A Multicenter Study

Background and Aims

Most microvascular invasion (MVI)-predicting models have not considered MVI classification, and thus do not reflect true MVI effects on prognosis of patients with hepatocellular carcinoma (HCC). We aimed to develop a novel MVI-predicting model focused on MVI classification, hoping to provide useful information for clinical treatment strategy decision-making.

Methods

A retrospective study was conducted with data from two Chinese medical centers for 800 consecutive patients with HCC (derivation cohort) and 250 matched patients (external validation cohort). MVI-associated variables were identified by ordinal logistic regression. Predictive models were constructed based on multivariate analysis results and validated internally and externally. The models' discriminative ability and calibration ability were examined.

Results

Four factors associated independently with MVI: tumor diameter, tumor number, serum lactate dehydrogenase (LDH) ≥ 176.58 U/L, and γ-glutamyl transpeptidase (γ-GGT). Area under the curve (AUC)s for our M2, M1, and M0 nomograms were 0.864, 0.648, and 0.782. Internal validation of all three models was confirmed with AUC analyses in D-sets (development datasets) and V-sets (validation datasets) and C-indices for each cohort. GiViTI calibration belt plots and Hosmer-Lemeshow (HL) chi-squared calibration values demonstrated good consistency between observed frequencies and predicted probabilities for the M2 and M0 nomograms. Although the M1 nomogram was well calibrated, its discrimination was poor.

Conclusion

We developed and validated MVI prediction models in patients with HCC that differentiate MVI classification and may provide useful guidance for treatment planning.

Histogram peritumoral enhanced features on MRI arterial phase with extracellular contrast agent can improve prediction of microvascular invasion of hepatocellular carcinoma

BACKGROUND

Preoperative microvascular invasion (MVI) prediction plays an important role in therapeutic decision-making of hepatocellular carcinoma (HCC). This study aimed to investigate the value of histogram based on the arterial phase (AP) of magnetic resonance imaging (MRI) with extracellular contrast agent compared with radiological features for predicting MVI of solitary HCC.

METHODS

In total, 113 patients with pathologically proven solitary HCC were retrospectively enrolled who received surgical resection and underwent preoperative abdominal MRI. The patients were divided into the ≤3 cm [small HCC (sHCC)] cohort and the >3 cm cohort. Based on pathological analysis of surgical specimens, the patients were classified into MVI negative (MVI-) and MVI positive (MVI+) groups. Peritumoral and intratumoral histogram features [mean, median, standard deviation (Std), coefficient of variation (CV), skewness, kurtosis] were acquired on AP subtraction images and radiological features [size, capsule, corona enhancement, corona enhancement thickness (CET), CET group]. Receiver operating characteristic (ROC) curve was constructed to assess predictive capability. Subgroup analysis of patients with a visible corona enhancement based on the CET cut-off value was performed.

RESULTS

None of the features extracted from the intratumor area were significantly different between the MVI+ and MVI- groups in both cohorts. Histogram defined peritumoral (peri-) mean, median, kurtosis, and radiological features including CET and CET group were associated with MVI in sHCCs. Peri-mean, median, Std and radiological features including incomplete capsule, CET, and CET group were associated with MVI in HCC >3 cm. In multivariate logistic regression analysis, the CET group and peri-mean were independent predictors for HCC >3 cm with an area under the curve (AUC) of 0.741. Peri-mean was an independent predictor for sHCC (AUC =0.798). Subgroup analysis of the corona enhancement using 8 mm as a cut-off value showed 100% sensitivity and negative predictive value (NPV).

CONCLUSIONS

Peritumoral AP enhanced degree on MRI showed an encouraging predictive performance for preoperative prediction of MVI, especially in sHCCs. CET ≤8 mm could be used as a negative predictive marker for MVI.

Prognosis after Curative Resection of Single Hepatocellular Carcinoma with A Focus on LI-RADS Targetoid Appearance on Preoperative Gadoxetic Acid-Enhanced MRI

Objective

To evaluate the prognostic implications of preoperative magnetic resonance imaging (MRI) features of hepatocellular carcinoma (HCC) with a focus on those with targetoid appearance based on the Liver Imaging Reporting and Data System (LI-RADS), as well as known microvascular invasion (MVI) features.

Materials and Methods

This retrospective study included 242 patients (190 male; mean age, 57.1 years) who underwent surgical resection of a single HCC (≤ 5 cm) as well as preoperative gadoxetic acid-enhanced MRI between January 2012 and March 2015. LI-RADS category was assigned, and the LR-M category was further classified into two groups according to rim arterial-phase hyperenhancement (APHE). The imaging features associated with MVI were also assessed. The overall survival (OS), recurrence-free survival (RFS), and their associated factors were evaluated.

Results

Among the 242 HCCs, 190 (78.5%), 25 (10.3%), and 27 (11.2%) were classified as LR-4/5, LR-M with rim APHE, and LR-M without rim APHE, respectively. LR-M with rim APHE (vs. LR-4/5; hazard ratio [HR] for OS, 5.48 [p = 0.002]; HR for RFS, 2.09 [p = 0.042]) and tumor size (per cm increase; HR for OS, 6.04 [p = 0.009]; HR for RFS, 1.77 [p = 0.014]) but not MVI imaging features (p > 0.05) were independent factors associated with OS and RFS. Compared to the 5-year OS and RFS rates in the LR-4/5 group (93.9% and 66.8%, respectively), the LR-M with rim APHE group had significantly lower rates (68.0% and 45.8%, respectively, both p < 0.05), while the LR-M without rim APHE group did not significantly differ in the survival rates (91.3% and 80.2%, respectively, both p > 0.05).

Conclusion

Further classification of LR-M according to the presence of rim APHE may help predict the postoperative prognosis of patients with a single HCC.

Intraprocedurally EOB-MRI/US fusion imaging focusing on hepatobiliary phase findings can help to reduce the recurrence of hepatocellular carcinoma after radiofrequency ablation

BACKGROUND & AIMS

To explore the ability of gadolinium-ethoxybenzyl-diethylenetriamine pentaacetic acid magnetic resonance imaging (EOB-MRI)/ultrasound (US) fusion imaging (FI) to improve the prognosis of radiofrequency ablation (RFA) by ablating the characteristic findings of hepatocellular carcinoma (HCC) in hepatobiliary phase (HBP) imaging.

METHODS

We retrospectively recruited 115 solitary HCC lesions with size of (15.9 ± 4.6) mm. They were all treated by RFA and preoperative EOB-MRI. According to the modalities guiding RFA performance, the lesions were grouped into contrast enhanced US (CEUS)/US guidance group and EOB-MRI/US FI guidance group. For the latter group, the ablation scope was set to cover the HBP findings (peritumoral hypointensity and irregular protruding margin). The presence of HBP findings, the modalities guided RFA, the recurrence rate were observed.

RESULTS

After an average follow-up of 377 days, local tumor progression (LTP) and intrahepatic distant recurrence (IDR) were 14.8% and 38.4%, respectively. The lesions having HBP findings exhibited a higher recurrence rate (73.7%) than the lesions without HBP findings (42.9%) (p = 0.002) and a low overall recurrence-free curve using the Kaplan-Meier method (p = 0.038). Using EOB-MRI/US FI as guidance, there was no difference in the recurrence rate between the groups with and without HBP findings (p = 0.799). In lesions with HBP findings, RFA guided by EOB-MRI/US FI (53.8%) produced a lower recurrence rate than CEUS/US (84.0%) (p = 0.045).

CONCLUSIONS

The intraprocedurally application of EOB-MRI/US FI to determine ablation scope according to HBP findings is feasible and beneficial for prognosis of RFA.

Revisiting Surgical Strategies for Hepatocellular Carcinoma With Microvascular Invasion

Although liver resection (LR) and liver transplantation (LT) are widely considered as potentially curative therapies for selected patients with hepatocellular carcinoma (HCC); however, there is still high risk of tumor recurrence in majority of HCC patients. Previous studies demonstrated that the presence of microvascular invasion (MVI), which was defined as the presence of tumor emboli within the vessels adjacent to HCC, was one of the key factors of early HCC recurrence and poor surgical outcomes after LR or LT. In this review, we evaluated the impact of current MVI status on surgical outcomes after curative therapies and aimed to explore the surgical strategies for HCC based on different MVI status with evidence from pathological examination. Surgical outcomes of HCC patients with MVI have been described as a varied range after curative therapies due to a broad spectrum of current definitions for MVI. Therefore, an international consensus on the validated definition of MVI in HCC is urgently needed to provide a more consistent evaluation and reliable prediction of surgical outcomes for HCC patients after curative treatments. We concluded that MVI should be further sub-classified into MI (microvessel invasion) and MPVI (microscopic portal vein invasion); for HCC patients with MPVI, local R0 resection with a narrow or wide surgical margin will get the same surgical results. However, for HCC patients with MI, local surgical resection with a wide and negative surgical margin will get better surgical outcomes. Nowadays, MVI status can only be reliably confirmed by histopathologic evaluation of surgical specimens, limiting its clinical application. Taken together, preoperative assessment of MVI is of utmost significance for selecting a reasonable surgical modality and greatly improving the surgical outcomes of HCC patients, especially in those with liver cirrhosis.

Potential application of dynamic contrast enhanced ultrasound in predicting microvascular invasion of hepatocellular carcinoma

OBJECTIVE

To investigate the clinical value of dynamic contrast enhanced ultrasound (D-CEUS) in predicting the microvascular invasion (MVI) of hepatocellular carcinoma (HCC).

PATIENTS AND METHODS

In this retrospective study, 16 patients with surgery and histopathologically proved HCC lesions were included. Patients were classified according to the presence of MVI: MVI positive group (n = 6) and MVI negative group (n = 10). Contrast enhanced ultrasound (CEUS) examinations were performed within a week before surgery. Dynamic analysis was performed by VueBox® software (Bracco, Italy). Three regions of interests (ROIs) were set in the center of HCC lesions, at the margin of HCC lesions and in the surrounding liver parenchyma accordingly. Time intensity curves (TICs) were generated and quantitative perfusion parameters including WiR (wash-in rate), WoR (wash-out rate), WiAUC (wash-in area under the curve), WoAUC (wash-out area under the curve) and WiPi (wash-in perfusion index) were obtained and analyzed.

RESULTS

All of HCC lesions showed arterial hyperenhancement (100 %) and at the late phase as hypoenhancement (75%) in CEUS. Among all CEUS quantitative parameters, the WiAUC and WoAUC were higher in MVI positive group than in MVI negative group in the center HCC lesions (P < 0.05), WiAUC, WoAUC and WiPI were higher in MVI positive group than in MVI negative group at the margin of HCC lesions. WiR and WoR were significant higher in MVI positive group.

CONCLUSIONS

D-CEUS with quantitative perfusion analysis has potential clinical value in predicting the existence of MVI in HCC lesions.

Using deep learning to predict microvascular invasion in hepatocellular carcinoma based on dynamic contrast-enhanced MRI combined with clinical parameters

PURPOSE

Microvascular invasion (MVI) is a critical determinant of the early recurrence and poor prognosis of patients with hepatocellular carcinoma (HCC). Prediction of MVI status is clinically significant for the decision of treatment strategies and the assessment of patient's prognosis. A deep learning (DL) model was developed to predict the MVI status and grade in HCC patients based on preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and clinical parameters.

METHODS

HCC patients with pathologically confirmed MVI status from January to December 2016 were enrolled and preoperative DCE-MRI of these patients were collected in this study. Then they were randomly divided into the training and testing cohorts. A DL model with eight conventional neural network (CNN) branches for eight MRI sequences was built to predict the presence of MVI, and further combined with clinical parameters for better prediction.

RESULTS

Among 601 HCC patients, 376 patients were pathologically MVI absent, and 225 patients were MVI present. To predict the presence of MVI, the DL model based only on images achieved an area under curve (AUC) of 0.915 in the testing cohort as compared to the radiomics model with an AUC of 0.731. The DL combined with clinical parameters (DLC) model yielded the best predictive performance with an AUC of 0.931. For the MVI-grade stratification, the DLC models achieved an overall accuracy of 0.793. Survival analysis demonstrated that the patients with DLC-predicted MVI status were associated with the poor overall survival (OS) and recurrence-free survival (RFS). Further investigation showed that hepatectomy with the wide resection margin contributes to better OS and RFS in the DLC-predicted MVI present patients.

CONCLUSION

The proposed DLC model can provide a non-invasive approach to evaluate MVI before surgery, which can help surgeons make decisions of surgical strategies and assess patient's prognosis.

Magnetic resonance severity index assessed by T1-weighted imaging for acute pancreatitis: correlation with clinical outcomes and grading of the revised Atlanta classification-a narrative review

Acute pancreatitis (AP) is a common disease that may involve pancreas and peripancreatic tissues with a prevalence of up to 50 per 100,000 individuals for year. The Atlanta classification was assessed for the first time in 1992 and modified in 2012 in order to describe morphological features of AP and its complications. AP can be morphologically distinguished in two main types: interstitial edematous pancreatitis (IEP) and necrotizing pancreatitis (NEP). This classification is very important because the presence of necrosis is directly linked to local or systemic complications, hospital stays and death. Magnetic resonance (MR) is very useful to characterize morphological features in AP and its abdominal complications. Particularly we would like to underline the diagnostic, staging and prognostic role of T1-weighted images with fat suppression that could be significant to assess many features of the AP inflammatory process and its complications (detection of the pancreatic contour, pancreatic necrosis, presence of haemorrhage). Signs of inflammatory and edema are instead observed by T1-weighted images. MR cholangiopancreatography (MRCP) is necessary to study the main pancreatic duct and the extrahepatic biliary tract and contrast-enhancement magnetic resonance imaging (MRI) allows to assess the extent of necrosis and vascular injuries.

The importance of a nonsmooth tumor margin and incomplete tumor capsule in predicting HCC microvascular invasion on preoperative imaging examination: a systematic review and meta-analysis

OBJECTIVES

Microvascular invasion (MVI) is a key factor affecting the prognosis of hepatocellular carcinoma (HCC). Preoperative imaging plays an important role in the diagnosis of HCC, treatment planning and treatment evaluation, but it is still difficult to detect MVI directly. Whether the appearance of the tumor margin and the capsule on radiological images can predict MVI is still controversial. The aim of this study is to explore the correlation of the presence of MVI with the smoothness of the tumor margin and the integrity of the capsule in HCC.

MATERIALS AND METHODS

The PubMed, Embase, Medline, SCI and Cochrane Library databases up to January 2020. Heterogeneity among studies was assessed by sensitivity analysis, subgroup analysis and meta-regression, and the influence of threshold effects was also analyzed.

RESULTS

Eleven studies with 1618 patients were included. The results of the meta-analysis indicated that there was a significant relationship between MVI and nonsmooth tumor margin (DOR = 4.62 [2.73, 7.81]) and between MVI and incomplete tumor capsule (DOR = 2.25 [1.22, 4.15]); the sensitivity and specificity of these two parameters were 0.757 [0.602, 0.865], 0.597 [0.450, 0.728] and 0.646 [0.455, 0.800], 0.552 [0.419, 0.678], respectively. We drew the receiver operating characteristic (ROC) curves, and the area under curve (AUC) of the nonsmooth tumor margin variable for predicting MVI was 0.72 [0.69, 0.77], and the AUC of the incomplete tumor capsule variable for predicting MVI was 0.62 [0.58, 0.66].

CONCLUSION

Nonsmooth tumor margins and incomplete tumor capsules observed by imaging are important for the preoperative prediction of MVI in HCC.

Interobserver Variability and Diagnostic Performance of Gadoxetic Acid-enhanced MRI for Predicting Microvascular Invasion in Hepatocellular Carcinoma

Background Accurate identification of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) before treatment is critical for selecting a proper treatment strategy. Purpose To evaluate the interobserver agreement and the diagnostic performance of the MRI assessment of MVI in HCC according to the level of radiologist experience. Materials and Methods This retrospective study included 100 patients with surgically confirmed HCCs smaller than 5 cm who underwent gadoxetic acid-enhanced MRI between 2013 and 2016. Eight postfellowship radiologists (four with 7-13 years of experience [more experienced] and four with 3-6 years of experience [less experienced]) evaluated four imaging features (nonsmooth tumor margin, irregular rim-like enhancement in the arterial phase, peritumoral arterial phase hyperenhancement, peritumoral hepatobiliary phase hypointensity) and assigned the possibility of MVI. Interobserver agreement was determined by using Fleiss κ statistics according to reviewer experience and tumor size (≤3 cm vs >3 cm). With reference standards of histopathologic specimens, the diagnostic performance in the identification of MVI was assessed by using receiver operating characteristic curve analysis. Results In 100 patients (mean age, 58 years ± 10 [standard deviation]; 70 men) with 100 HCCs (mean size, 2.8 cm ± 0.9), 39 (39%) HCCs had MVI. The overall interobserver agreement was fair to moderate for the imaging features and their combinations (κ = 0.38-0.47) and MVI probability (κ = 0.41; 95% confidence interval: 0.33, 0.45). More experienced reviewers demonstrated higher agreement in MVI probability than less experienced reviewers (κ = 0.55 vs 0.36, respectively; P = .002). Diagnostic performance of each reviewer was modest for MVI prediction (area under the receiver operating characteristic curve [AUC] range, 0.60-0.74). The AUCs for the diagnosis of MVI were lower for HCCs larger than 3 cm (range, 0.55-0.69) than for those less than or equal to 3 cm (range, 0.59-0.75). Conclusion Considerable interobserver variability exists in the assessment of microvascular invasion in hepatocellular carcinoma using MRI, even for more experienced radiologists. © RSNA, 2020 Online supplemental material is available for this article. See also the editorial by Tang in this issue.

IVIM improves preoperative assessment of microvascular invasion in HCC

PURPOSE

To prospectively evaluate the potential role of intravoxel incoherent motion (IVIM) and conventional radiologic features for preoperative prediction of microvascular invasion (MVI) in patients with hepatocellular carcinoma (HCC).

METHODS

Institutional review board approval and written informed consent were obtained for this study. A cohort comprising 115 patients with 135 newly diagnosed HCCs between January 2016 and April 2017 were evaluated. Two radiologists independently reviewed the radiologic features and the apparent diffusion coefficient (ADC), true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion component fraction (f) were also measured. Interobserver agreement was checked and univariate and multivariate logistic regressions were used for screening the risk factors. Receiver operating characteristics (ROC) curve analyses were performed to evaluate the diagnostic performance.

RESULTS

Features significantly related to MVI of HCC at univariate analysis were reduced ADC (odds ratio, 0.341; 95% CI, 0.211-0.552; p < 0.001), D (odds ratio, 0.141; 95% CI, 0.067-0.299; p < 0.001), and irregular circumferential enhancement (odds ratio, 9.908; 95% CI, 3.776-25.996; p < 0.001). At multivariate analysis, only D value (odds ratio, 0.096; 95% CI, 0.025-0.364; p < 0.001) was the independent risk factor for MVI of HCC. The mean D value for MVI of HCC showed an area under ROC curves of 0.815 (95% CI, 0.740-0.877).

CONCLUSION

IVIM model-derived D value is superior to ADC measured with mono-exponential model for evaluating the MVI of HCC. Among MR imaging features, tumor margin, enhancement pattern, tumor capsule, and peritumoral enhancement were not predictive for MVI.

KEY POINTS

• Diffusion MRI is useful for non-invasively evaluating the microvascular invasion of hepatocellular carcinoma. • IVIM model is advantageous over mono-exponential model for assessing the microvascular invasion of hepatocellular carcinoma. • Decreased D value was the independent risk factor for predicting MVI of HCC.